code: def test_categorical_dist_shape():
      num_actions = 2
      # actions taken, of shape (batch_size, time_steps)
      actions = mx.nd.array((1,))
      # action probabilities, of shape (batch_size, time_steps, num_actions)
      policy_probs = mx.nd.array((0.8, 0.2))
      policy_dist = CategoricalDist(num_actions, policy_probs)
      action_probs = policy_dist.log_prob(actions)
      assert action_probs.ndim == 1
      assert action_probs.shape[0] == 1
  
imports: import mxnet
  import numpy
  import os
  import pytest
  from scipy import stats
  import sys
  from rl_coach.architectures.head_parameters import PPOHeadParameters
  from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist, DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 6 for calling a global function with example usage: test_categorical_dist_shape() without return types